An Editorial About Elemental Analysis
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEEditor's PageNEXTAn Editorial About Elemental AnalysisFrançois P. Gabbaï*†, Paul J. Chirik‡, Deryn E. Fogg§, Karsten Meyer⊥, Daniel J. Mindiola∥, Laurel L. Schafer#, and Shu-Li You&View Author Information† Department of Chemistry, Texas A&M University, College Station, Texas 77843-3255, United States‡ Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States§ Center for Catalysis Research & Innovation and Department of Chemistry and Biological Sciences, University of Ottawa, Ottawa, Ontario, Canada K1N 6N5⊥ Department of Chemistry and Pharmacy Inorganic Chemistry, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Egerlandstr. 1, 91058 Erlangen, Germany∥ Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States# Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, Canada V6T 1Z1& State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Lu, Shanghai 200032, People's Republic of China*E-mail for F.P.G.: [email protected]Cite this: Organometallics 2016, 35, 19, 3255–3256Publication Date (Web):September 23, 2016Publication History Published online23 September 2016Published inissue 10 October 2016https://pubs.acs.org/doi/10.1021/acs.organomet.6b00720https://doi.org/10.1021/acs.organomet.6b00720editorialACS PublicationsCopyright © 2016 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views8433Altmetric-Citations42LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (124 KB) Get e-AlertscloseSUBJECTS:High-performance liquid chromatography,Impurities,Nuclear magnetic resonance spectroscopy,Organometallic chemistry Get e-Alerts
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.184 | 0.173 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".